Machine learning for major food crops breeding: Applications, challenges, and ways forward
Document Type
Article
Publication Date
5-1-2024
Abstract
Increasing the production of the three major food crops (MFCs), maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum), is essential to fulfilling the food demand for the growing human population. Increasing food production may require the integration of machine learning (ML) into plant breeding programs. However, developing ML tools to improve the production of MFCs is a daunting task due to the lack of quality data and the computation resources needed to process this information. Hence, this review discusses the recent applications of ML for improving MFCs production, including plant phenotyping, yield forecasting, and candidate gene prediction. Based on the challenges reported in recent ML experiments for MFCs, this review prescribes solutions to produce scalable ML models. This review provides valuable insights for future studies and promotes collective efforts among researchers implementing ML to enhance MFCs productivity.
Keywords
Phenotype, Computer Vision, Image Processing
Divisions
InstituteofBiologicalSciences,MathematicalSciences
Funders
None
Publication Title
Agronomy Journal
Volume
116
Issue
3
Publisher
Wiley
Publisher Location
111 RIVER ST, HOBOKEN 07030-5774, NJ USA